Metagenomics reveals effects of fluctuating water conditions on functional pathways in plant litter microbial community.

Climate change modifies environmental conditions, resulting in altered precipitation patterns, moisture availability and nutrient distribution for microbial communities. Changes in water availability are projected to affect a range of ecological processes, including the decomposition of plant litter and carbon cycling. However, a detailed understanding of microbial stress response to drought/flooding is missing. In this study, an intermittent lake is taken up as a model for changes in water availability and how they affect the functional pathways in microbial communities of the decomposing Phragmites australis litter. The results show that most enriched functions in both habitats belonged to the classes of Carbohydrates and Clustering-based subsystems (terms with unknown function) from SEED subsystems classification. We confirmed that changes in water availability resulted in altered functional makeup of microbial communities. Our results indicate that microbial communities under more frequent water stress (due to fluctuating conditions) could sustain an additional metabolic cost due to the production or uptake of compatible solutes to maintain cellular osmotic balance. Nevertheless, although prolonged submergence seemed to have a negative impact on several functional traits in the fungal community, the decomposition rate was not affected.

The use of hydrodynamic cavitation for waste-to-energy approach to enhance methane production from waste activated sludge.

Anaerobic digestion in wastewater treatment plants converts its unwanted end product - waste activated sludge into biogas. Even if the process is well established, pre-treatment of the sludge can further improve its efficiency. In this study, four treatment regimes for increasing methane production through prior sludge disintegration were investigated using lab-scale cavitation generator and real sludge samples. Three different cavitating (attached cavitation regime, developed cloud shedding cavitation regime and cavitation in a wake regime) and one non-cavitating regime at elevated static pressure were studied in detail for their effectiveness on physical and chemical properties of sludge samples. Volume-weighted mean diameter D[4,3] of sludge's particles decreased by up to 92%, specific surface area increased by up to 611%, while viscosity (at a shear rate of 3.0 s-1) increased by up to 39% in the non-cavitating and decreased by up to 24% in all three cavitating regimes. Chemical changes were more pronounced in cavitating regimes, where released soluble chemical oxygen demand (sCOD) and increase of dissolved organic matter (DOM) compounds by up to 175% and 122% were achieved, respectively. Methane production increased in all four cases, with the highest increase of 70% corresponding to 312 mL CH4 g-1 COD. However, this treatment was not particularly efficient in terms of energy consumption. The best energy balance was found for the regime with a biochemical methane potencial increase of 43%.

Overview of data preprocessing for machine learning applications in human microbiome research.

Although metagenomic sequencing is now the preferred technique to study microbiome-host interactions, analyzing and interpreting microbiome sequencing data presents challenges primarily attributed to the statistical specificities of the data (e.g., sparse, over-dispersed, compositional, inter-variable dependency). This mini review explores preprocessing and transformation methods applied in recent human microbiome studies to address microbiome data analysis challenges. Our results indicate a limited adoption of transformation methods targeting the statistical characteristics of microbiome sequencing data. Instead, there is a prevalent usage of relative and normalization-based transformations that do not specifically account for the specific attributes of microbiome data. The information on preprocessing and transformations applied to the data before analysis was incomplete or missing in many publications, leading to reproducibility concerns, comparability issues, and questionable results. We hope this mini review will provide researchers and newcomers to the field of human microbiome research with an up-to-date point of reference for various data transformation tools and assist them in choosing the most suitable transformation method based on their research questions, objectives, and data characteristics.

Biodegradation of polysaccharide-based biocomposites with acetylated cellulose nanocrystals, alginate and chitosan in aqueous environment.

Biocomposite films from renewable sources are seen to be viable candidates as sustainable, zero-waste packaging materials. In this study, biocomposites films using chitosan and alginate as matrices, and pristine or acetylated cellulose nanocrystals (CNCs) as reinforcement agents, were fabricated, thoroughly characterized in terms of structure (with ATR-FTIR and XRD), morphology (SEM), thermal stability (TGA coupled with FTIR), water content and solubility and mechanical properties and subjected to controlled biological degradation in aqueous environment with added activated sludge. Biodegradation activity was followed through respirometry by measurement of change in partial O2 pressure using OxiTop® system. While the initial rate of biodegradation is higher in chitosan-based films with incorporated CNCs (both pristine and modified) compared to any other tested biocomposites, it was observed that chitosan-based films are not completely degradable in activated sludge medium, whereas alginate-based films reached complete biodegradation in 107 h to 112 h. Additional study of the aqueous medium with in situ FTIR during biodegradation offered an insight into biodegradation mechanisms. Use of advanced statistical methods indicated that selection of material (ALG vs CH) has the highest influence on biodegradability, followed by solubility of the material and its thermal stability.

Difficult to treat absence seizures in children: A single-center retrospective study.

Objectives: The aim of this study was to analyse the characteristics of typical absence seizures (AS), myoclonic AS and AS with eyelid myoclonia in children and to find associations between these characteristics and difficult to treat absence seizures (DTAS). Methods: This was a single-center retrospective study. Electronic health records of pediatric patients with a clinical diagnosis of AS treated at a single tertiary epilepsy center between January 2013 and June 2020 were reviewed. Clinical characteristics, seizure information, ASM, and therapeutic response of patients were recorded. All patients were followed up for at least 1 year. DTAS were defined as failure to achieve remission after treatment with at least 2 anti-seizure medications (ASM), regardless of whether remission was achieved eventually in the study period. Results: Data from 131 patients were available for analysis. Remission was achieved after the first ASM treatment in 81 (61.8%) patients, and eventually in 120 (91.6%) during the study period. Epilepsy was classified as DTAS in 18 (13.7%) patients. AS were more often difficult to treat in patients with myoclonic AS and AS with eyelid myoclonia (40.0%), compared with patients with typical AS (11.4%; p = 0.012, 95% CI 1.480-25.732). A positive family history of epilepsy (p = 0.046; 95% CI 1.021-8.572), a higher seizure frequency (p = 0.023, 95% CI 1.009-1.126) prior to ASM treatment, and longer time between seizure onset and treatment onset (p = 0.026; 95% CI 1.006-1.099) were also associated with DTAS. Significance: Our study suggests that several clinical characteristics of AS are associated with DTAS. One of these was the time between onset of AS and initiation of ASM treatment, which can be shortened with better care, suggesting that early diagnosis and treatment may improve prognosis in pediatric patients with AS. These findings remain to be confirmed in larger prospective studies.

How Can We Advance Integrative Biology Research in Animal Science in 21st Century? Experience at University of Ljubljana from 2002 to 2022.

In this perspective analysis, we strive to answer the following question: how can we advance integrative biology research in the 21st century with lessons from animal science? At the University of Ljubljana, Biotechnical Faculty, Department of Animal Science, we share here our three lessons learned in the two decades from 2002 to 2022 that we believe could inform integrative biology, systems science, and animal science scholarship in other countries and geographies. Cultivating multiomics knowledge through a conceptual lens of integrative biology is crucial for life sciences research that can stand the test of diverse biological, clinical, and ecological contexts. Moreover, in an era of the current COVID-19 pandemic, animal nutrition and animal science, and the study of their interactions with human health (and vice versa) through integrative biology approaches hold enormous prospects and significance for systems medicine and ecosystem health.

Exercise and Interorgan Communication: Short-Term Exercise Training Blunts Differences in Consecutive Daily Urine 1H-NMR Metabolomic Signatures between Physically Active and Inactive Individuals.

Physical inactivity is a worldwide health problem, an important risk for global mortality and is associated with chronic noncommunicable diseases. The aim of this study was to explore the differences in systemic urine 1H-NMR metabolomes between physically active and inactive healthy young males enrolled in the X-Adapt project in response to controlled exercise (before and after the 3-day exercise testing and 10-day training protocol) in normoxic (21% O2), normobaric (~1000 hPa) and normal-temperature (23 °C) conditions at 1 h of 50% maximal pedaling power output (Wpeak) per day. Interrogation of the exercise database established from past X-Adapt results showed that significant multivariate differences existed in physiological traits between trained and untrained groups before and after training sessions and were mirrored in significant differences in urine pH, salinity, total dissolved solids and conductivity. Cholate, tartrate, cadaverine, lysine and N6-acetyllisine were the most important metabolites distinguishing trained and untrained groups. The relatively little effort of 1 h 50% Wpeak per day invested by the untrained effectively modified their resting urine metabolome into one indistinguishable from the trained group, which hence provides a good basis for the planning of future recommendations for health maintenance in adults, irrespective of the starting fitness value. Finally, the 3-day sessions of morning urine samples represent a good candidate biological matrix for future delineations of active and inactive lifestyles detecting differences unobservable by single-day sampling due to day-to-day variability.

Urine and Fecal 1H-NMR Metabolomes Differ Significantly between Pre-Term and Full-Term Born Physically Fit Healthy Adult Males.

Preterm birth (before 37 weeks gestation) accounts for ~10% of births worldwide and remains one of the leading causes of death in children under 5 years of age. Preterm born adults have been consistently shown to be at an increased risk for chronic disorders including cardiovascular, endocrine/metabolic, respiratory, renal, neurologic, and psychiatric disorders that result in increased death risk. Oxidative stress was shown to be an important risk factor for hypertension, metabolic syndrome and lung disease (reduced pulmonary function, long-term obstructive pulmonary disease, respiratory infections, and sleep disturbances). The aim of this study was to explore the differences between preterm and full-term male participants' levels of urine and fecal proton nuclear magnetic resonance (1H-NMR) metabolomes, during rest and exercise in normoxia and hypoxia and to assess general differences in human gut-microbiomes through metagenomics at the level of taxonomy, diversity, functional genes, enzymatic reactions, metabolic pathways and predicted gut metabolites. Significant differences existed between the two groups based on the analysis of 1H-NMR urine and fecal metabolomes and their respective metabolic pathways, enabling the elucidation of a complex set of microbiome related metabolic biomarkers, supporting the idea of distinct host-microbiome interactions between the two groups and enabling the efficient classification of samples; however, this could not be directed to specific taxonomic characteristics.

Microbial response on the first full-scale DEMON® biomass transfer for mainstream deammonification.

Sidestream partial nitritation and deammonification (pN/A) of high-strength ammonia wastewater is a well-established technology. Its expansion to the mainstream is, however mainly impeded by poor retention of anaerobic ammonia oxidizing bacteria (AnAOB), insufficient repression of nitrite oxidizing bacteria (NOB) and difficult control of soluble chemical oxygen demand and nitrite levels. At the municipal wastewater treatment plant in Strass (Austria) the microbial consortium was exhaustively monitored at full-scale over one and a half year with regular transfer of sidestream DEMON® biomass and further retention and enrichment of granular anammox biomass via hydrocyclone operation. Routine process parameters were surveyed and the response and evolution of the microbiota was followed by molecular tools, ex-situ activity tests and further, AnAOB quantification through particle tracking and heme measurement. After eight months of operation, the first anaerobic, simultaneous depletion of ammonia and nitrite was observed ex-situ, together with a direction to higher nitrite generation (68% of total NOx-N) as compared to nitrate under aerobic conditions. Our dissolved oxygen (DO) scheme allowed for transient anoxic conditions and had a strong influence on nitrite levels and the NOB community, where Nitrobacter eventually dominated Nitrospira. The establishment of a minor but stable AnAOB biomass was accompanied by the rise of Chloroflexi and distinct emergence of Chlorobi, a trend not seen in the sidestream system. Interestingly, the most pronounced switch in the microbial community and noticeable NOB repression occurred during unfavorable conditions, i.e. the cold winter season and high organic load. Further abatement of NOB was achieved through bioaugmentation of aerobic ammonia oxidizing bacteria (AerAOB) from the sidestream-DEMON® tank. Performance of the sidestream pN/A was not impaired by this operational scheme and the average volumetric nitrogen removal rate of the mainstream even doubled in the second half of the monitoring campaign. We conclude that a combination of both, regular sidestream-DEMON® biomass transfer and granular SRT increase via hydrocyclone operation was crucial for AnAOB establishment within the mainstream.

Original Leaf Colonisers Shape Fungal Decomposer Communities of Phragmites australis in Intermittent Habitats.

Common reed (Phragmites australis) has high biomass production and is primarily subjected to decomposition processes affected by multiple factors. To predict litter decomposition dynamics in intermittent lakes, it is critical to understand how communities of fungi, as the primary decomposers, form under different habitat conditions. This study reports the shotgun metagenomic sequencing of the initial fungal communities on common reed leaves decomposing under different environmental conditions. We demonstrate that a complex network of fungi forms already on the plant persists into the decomposition phase. Phragmites australis leaves contained at least five fungal phyla, with abundant Ascomycota (95.7%) and Basidiomycota (4.1%), identified as saprotrophs (48.6%), pathotrophs (22.5%), and symbiotrophs (12.6%). Most of the correlations between fungi in fresh and decomposing leaves were identified as co-occurrences (positive correlations). The geographic source of litter and leaf age did not affect the structure and diversity of fungal communities. Keystone taxa were mostly moisture-sensitive. Our results suggest that habitat has a strong effect on the formation of the fungal communities through keystone taxa. Nevertheless, it can also alter the proportions of individual fungal groups in the community through indirect effects on competition between the fungal taxa and their exploitation of favourable conditions.

Integral analysis of hydrodynamic cavitation effects on waste activated sludge characteristics, potentially toxic metals, microorganisms and identification of microplastics.

Wastewater treatment plants, the last barrier between ever-increasing human activities and the environment, produce huge amounts, of unwanted semi-solid by-product - waste activated sludge. Anaerobic digestion can be used to reduce the amount of sludge. However, the process needs extensive modernisation and refinement to realize its full potential. This can be achieved by using efficient pre-treatment processes that result in high sludge disintegration and solubilization. To this end, we investigated the efficiency of a novel pinned disc rotational generator of hydrodynamic cavitation. The results of physical and chemical evaluation showed a reduction in mean particle size up to 88%, an increase in specific surface area up to 300% and an increase in soluble COD, NH4-N, NO3-N, PO4-P up to 155.8, 126.3, 250 and 29.7%, respectively. Microscopic images confirmed flocs disruption and damage to yeast cells and Epistilys species due to mechanical effects of cavitation such as microjets and shear forces. The observed cell ruptures and cracks were sufficient for the release of small soluble biologically relevant dissolved organic molecules into the bulk liquid, but not for the release of microbial DNA. Cavitation treatment also decreased total Pb concentrations by 70%, which was attributed to the reactions triggered by the chemical effects of cavitation. Additionally, the study confirmed the presence of microplastic particles and fibers of polyethylene, polyethylene terephthalate, polypropylene, and nylon 6 in the waste activated sludge.

General Unified Microbiome Profiling Pipeline (GUMPP) for Large Scale, Streamlined and Reproducible Analysis of Bacterial 16S rRNA Data to Predicted Microbial Metagenomes, Enzymatic Reactions and Metabolic Pathways.

General Unified Microbiome Profiling Pipeline (GUMPP) was developed for large scale, streamlined and reproducible analysis of bacterial 16S rRNA data and prediction of microbial metagenomes, enzymatic reactions and metabolic pathways from amplicon data. GUMPP workflow introduces reproducible data analyses at each of the three levels of resolution (genus; operational taxonomic units (OTUs); amplicon sequence variants (ASVs)). The ability to support reproducible analyses enables production of datasets that ultimately identify the biochemical pathways characteristic of disease pathology. These datasets coupled to biostatistics and mathematical approaches of machine learning can play a significant role in extraction of truly significant and meaningful information from a wide set of 16S rRNA datasets. The adoption of GUMPP in the gut-microbiota related research enables focusing on the generation of novel biomarkers that can lead to the development of mechanistic hypotheses applicable to the development of novel therapies in personalized medicine.

Broad diversity of bacteria degrading 17ß-estradiol-3-sulfate isolated from river sediment and biofilm at a wastewater treatment plant discharge.

Conjugated estrogens, such as 17ß-estradiol-3-sulfate (E2-3S), can be released into aquatic environments through wastewater treatment plants (WWTP). There, they are microbiologically degraded into free estrogens, which can have harmful effects on aquatic wildlife. Here, the degradation of E2-3S in environmental samples taken upstream, downstream and at the effluent of a WWTP was assessed. Sediment and biofilm samples were enriched for E2-3S-degrading microorganisms, yielding a broad diversity of bacterial isolates, including known and novel degraders of estrogens. Since E2-3S-degrading bacteria were also isolated in the sample upstream of the WWTP, the WWTP does not influence the ability of the microbial community to degrade E2-3S.

Spinal Muscular Atrophy after Nusinersen Therapy: Improved Physiology in Pediatric Patients with No Significant Change in Urine, Serum, and Liquor 1H-NMR Metabolomes in Comparison to an Age-Matched, Healthy Cohort.

Spinal muscular atrophy (SMA) is a genetically heterogeneous group of rare neuromuscular diseases and was until recently the most common genetic cause of death in children. The effects of 2-month nusinersen therapy on urine, serum, and liquor 1H-NMR metabolomes in SMA males and females were not explored yet, especially not in comparison to the urine 1H-NMR metabolomes of matching male and female cohorts. In this prospective, single-centered study, urine, serum, and liquor samples were collected from 25 male and female pediatric patients with SMA before and after 2 months of nusinersen therapy and urine samples from a matching healthy cohort (n = 125). Nusinersen intrathecal application was the first therapy for the treatment of SMA by the Food and Drug Administration (FDA) and the European Medicines Agency (EMA). Metabolomes were analyzed using targeted metabolomics utilizing 600 MHz 1H-NMR, parametric and nonparametric multivariate statistical analyses, machine learning, and modeling. Medical assessment before and after nusinersen therapy showed significant improvements of movement, posture, and strength according to various medical tests. No significant differences were found in metabolomes before and after nusinersen therapy in urine, serum, and liquor samples using an ensemble of statistical and machine learning approaches. In comparison to a healthy cohort, 1H-NMR metabolomes of SMA patients contained a reduced number and concentration of urine metabolites and differed significantly between males and females as well. Significantly larger data scatter was observed for SMA patients in comparison to matched healthy controls. Machine learning confirmed urinary creatinine as the most significant, distinguishing SMA patients from the healthy cohort. The positive effects of nusinersen therapy clearly preceded or took place devoid of significant rearrangements in the 1H-NMR metabolomic makeup of serum, urine, and liquor. Urine creatinine was successful at distinguishing SMA patients from the matched healthy cohort, which is a simple systemic novelty linking creatinine and SMA to the physiology of inactivity and diabetes, and it facilitates the monitoring of SMA disease in pediatric patients through non-invasive urine collection.

The Importance of Objective Stool Classification in Fecal 1H-NMR Metabolomics: Exponential Increase in Stool Crosslinking Is Mirrored in Systemic Inflammation and Associated to Fecal Acetate and Methionine.

Past studies strongly connected stool consistency-as measured by Bristol Stool Scale (BSS)-with microbial gene richness and intestinal inflammation, colonic transit time and metabolome characteristics that are of clinical relevance in numerous gastro intestinal conditions. While retention time, defecation rate, BSS but not water activity have been shown to account for BSS-associated inflammatory effects, the potential correlation with the strength of a gel in the context of intestinal forces, abrasion, mucus imprinting, fecal pore clogging remains unexplored as a shaping factor for intestinal inflammation and has yet to be determined. Our study introduced a minimal pressure approach (MP) by probe indentation as measure of stool material crosslinking in fecal samples. Results reported here were obtained from 170 samples collected in two independent projects, including males and females, covering a wide span of moisture contents and BSS. MP values increased exponentially with increasing consistency (i.e., lower BSS) and enabled stratification of samples exhibiting mixed BSS classes. A trade-off between lowest MP and highest dry matter content delineated the span of intermediate healthy density of gel crosslinks. The crossectional transects identified fecal surface layers with exceptionally high MP and of <5 mm thickness followed by internal structures with an order of magnitude lower MP, characteristic of healthy stool consistency. The MP and BSS values reported in this study were coupled to reanalysis of the PlanHab data and fecal 1H-NMR metabolomes reported before. The exponential association between stool consistency and MP determined in this study was mirrored in the elevated intestinal and also systemic inflammation and other detrimental physiological deconditioning effects observed in the PlanHab participants reported before. The MP approach described in this study can be used to better understand fecal hardness and its relationships to human health as it provides a simple, fine scale and objective stool classification approach for the characterization of the exact sampling locations in future microbiome and metabolome studies.

An Early Stage Researcher's Primer on Systems Medicine Terminology.

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic.

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment.

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.

Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions.

The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 "ML4Microbiome" that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.